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1.
BMJ Open ; 11(1): e042945, 2021 01 26.
Artigo em Inglês | MEDLINE | ID: mdl-33500288

RESUMO

OBJECTIVE: In this study, we describe the pattern of bed occupancy across England during the peak of the first wave of the COVID-19 pandemic. DESIGN: Descriptive survey. SETTING: All non-specialist secondary care providers in England from 27 March27to 5 June 2020. PARTICIPANTS: Acute (non-specialist) trusts with a type 1 (ie, 24 hours/day, consultant-led) accident and emergency department (n=125), Nightingale (field) hospitals (n=7) and independent sector secondary care providers (n=195). MAIN OUTCOME MEASURES: Two thresholds for 'safe occupancy' were used: 85% as per the Royal College of Emergency Medicine and 92% as per NHS Improvement. RESULTS: At peak availability, there were 2711 additional beds compatible with mechanical ventilation across England, reflecting a 53% increase in capacity, and occupancy never exceeded 62%. A consequence of the repurposing of beds meant that at the trough there were 8.7% (8508) fewer general and acute beds across England, but occupancy never exceeded 72%. The closest to full occupancy of general and acute bed (surge) capacity that any trust in England reached was 99.8% . For beds compatible with mechanical ventilation there were 326 trust-days (3.7%) spent above 85% of surge capacity and 154 trust-days (1.8%) spent above 92%. 23 trusts spent a cumulative 81 days at 100% saturation of their surge ventilator bed capacity (median number of days per trust=1, range: 1-17). However, only three sustainability and transformation partnerships (aggregates of geographically co-located trusts) reached 100% saturation of their mechanical ventilation beds. CONCLUSIONS: Throughout the first wave of the pandemic, an adequate supply of all bed types existed at a national level. However, due to an unequal distribution of bed utilisation, many trusts spent a significant period operating above 'safe-occupancy' thresholds despite substantial capacity in geographically co-located trusts, a key operational issue to address in preparing for future waves.


Assuntos
/epidemiologia , Número de Leitos em Hospital , Hospitais/provisão & distribução , Capacidade de Resposta ante Emergências , Ventiladores Mecânicos/provisão & distribução , Ocupação de Leitos/estatística & dados numéricos , Inglaterra/epidemiologia , Pessoal de Saúde , Humanos , Unidades de Terapia Intensiva/provisão & distribução , Medicina Estatal
2.
J Crit Care ; 62: 172-175, 2021 04.
Artigo em Inglês | MEDLINE | ID: mdl-33385774

RESUMO

COVID-19 has created an enormous health crisis and this spring New York City had a severe outbreak that pushed health and critical care resources to the limit. A lack of adequate space for mechanically ventilated patients induced our hospital to convert operating rooms into critical care areas (OR-ICU). A large number of COVID-19 will develop acute kidney injury that requires renal replacement therapy (RRT). We included 116 patients with COVID-19 who required mechanical ventilation and were cared for in our OR-ICU. At 90 days and at discharge 35 patients died (30.2%). RRT was required by 45 of the 116 patients (38.8%) and 18 of these 45 patients (40%) compared to 17 with no RRT (23.9%, ns) died during hospitalization and after 90 days. Only two of the 27 patients who required RRT and survived required RRT at discharge and 90 days. When defining renal recovery as a discharge serum creatinine within 150% of baseline, 68 of 78 survivors showed renal recovery (87.2%). Survival was similar to previous reports of patients with severe COVID-19 for patients cared for in provisional ICUs compared to standard ICUs. Most patients with severe COVID-19 and AKI are likely to recover full renal function.


Assuntos
Lesão Renal Aguda/etiologia , Lesão Renal Aguda/mortalidade , Lesão Renal Aguda/terapia , /mortalidade , Terapia de Substituição Renal , Idoso , Estudos de Coortes , Feminino , Hospitalização , Humanos , Unidades de Terapia Intensiva/provisão & distribução , Masculino , Pessoa de Meia-Idade , Cidade de Nova Iorque/epidemiologia , Recuperação de Função Fisiológica , Respiração Artificial , Estudos Retrospectivos
3.
Rev. bioét. derecho ; (50): 99-112, nov. 2020.
Artigo em Espanhol | IBECS | ID: ibc-191348

RESUMO

En el marco de una pandemia a escala mundial como la que representa el COVID-19 uno de los mayores dilemas bioéticos que se plantean es el de la gestión de determinados medios asistenciales escasos, tales como los respiradores (ventilación mecánica asistida), pues del acceso a los mismos dependen las posibilidades de supervivencia de numerosos pacientes en estado crítico. El presente trabajo trata de determinar los criterios para la gestión de dichos soportes vitales en un contexto de escasez extrema de los mismos para hacer frente a las necesidades de la totalidad de los pacientes que los requieren, analizando la literatura comparada sobre el particular, así como diferentes informes institucionales y de organismos en la esfera de la bioética


In the context of a worldwide pandemic such as COVID-19, one of the greatest bioethical dilemmas that arise is the management of certain scarce medical devices, such as ventilators (mechanical ventilation), since the survival of many critically ill patients depends on the access to these ventilators. The present paper tries to determine the criteria applicable for the management of these medical devices in a context of extreme scarcity to face the needs of all the patients who require them. To this end, the comparative literature on the subject as well as different institutional and academic reports in the field of bioethics are analysed


En el marc d'una pandèmia a escala mundial com la que representa la COVID-19 un dels majors dilemes bioètics que es plantegen és el de la gestió de determinats mitjans assistencials escassos, com ara els respiradors (ventilació mecànica assistida), ja que de l'accés als mateixos depenen les possibilitats de supervivència de nombrosos pacients en estat crític. El present treball pretén determinar els criteris per a la gestió d'aquests suports vitals en un context d'escassetat extrema dels mateixos per fer front a les necessitats de la totalitat dels pacients que els requereixen, analitzant la literatura comparada sobre el particular, així com diferents informes institucionals I d'organismes en l'esfera de la bioètica


Assuntos
Humanos , Sistemas de Manutenção da Vida/estatística & dados numéricos , Administração dos Cuidados ao Paciente , Infecções por Coronavirus/terapia , Pneumonia Viral/terapia , Pandemias , Triagem , Respiração Artificial , Ventiladores Mecânicos/provisão & distribução , Unidades de Terapia Intensiva/provisão & distribução
4.
Elife ; 92020 10 12.
Artigo em Inglês | MEDLINE | ID: mdl-33044170

RESUMO

This study examined records of 2566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively. Pneumonia severity scores achieve respective accuracies of 73% and 74% for ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease.


Assuntos
Betacoronavirus , Infecções por Coronavirus/terapia , Necessidades e Demandas de Serviços de Saúde , Pandemias , Pneumonia Viral/terapia , Adulto , Idoso , Área Sob a Curva , Índice de Massa Corporal , Comorbidade , Infecções por Coronavirus/epidemiologia , Diabetes Mellitus/epidemiologia , Feminino , Hospitalização/estatística & dados numéricos , Humanos , Unidades de Terapia Intensiva/estatística & dados numéricos , Unidades de Terapia Intensiva/provisão & distribução , Masculino , Massachusetts/epidemiologia , Pessoa de Meia-Idade , Dinâmica não Linear , Pneumonia Viral/epidemiologia , Utilização de Procedimentos e Técnicas , Curva ROC , Respiração Artificial/estatística & dados numéricos , Fatores de Risco , Ventiladores Mecânicos/provisão & distribução
5.
Medwave ; 20(9): e8039, 2020 Oct 05.
Artigo em Espanhol | MEDLINE | ID: mdl-33031358

RESUMO

Introduction: SARS CoV-2 pandemic is pressing hard on the responsiveness of health systems worldwide, notably concerning the massive surge in demand for intensive care hospital beds. Aim: This study proposes a methodology to estimate the saturation moment of hospital intensive care beds (critical care beds) and determine the number of units required to compensate for this saturation. Methods: A total of 22,016 patients with diagnostic confirmation for COVID-19 caused by SARS-CoV-2 were analyzed between March 4 and May 5, 2020, nationwide. Based on information from the Chilean Ministry of Health and ministerial announcements in the media, the overall availability of critical care beds was estimated at 1,900 to 2,000. The Gompertz function was used to estimate the expected number of COVID-19 patients and to assess their exposure to the available supply of intensive care beds in various possible scenarios, taking into account the supply of total critical care beds, the average occupational index, and the demand for COVID-19 patients who would require an intensive care bed. Results: A 100% occupancy of critical care beds could be reached between May 11 and May 27. This condition could be extended for around 48 days, depending on how the expected over-demand is managed. Conclusion: A simple, easily interpretable, and applicable to all levels (nationwide, regionwide, municipalities, and hospitals) model is offered as a contribution to managing the expected demand for the coming weeks and helping reduce the adverse effects of the COVID-19 pandemic.


Assuntos
Infecções por Coronavirus/epidemiologia , Número de Leitos em Hospital/estatística & dados numéricos , Unidades de Terapia Intensiva/provisão & distribução , Modelos Estatísticos , Pneumonia Viral/epidemiologia , Chile/epidemiologia , Humanos , Pandemias
6.
Anesth Analg ; 131(5): 1337-1341, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-33079852

RESUMO

BACKGROUND: In response to the coronavirus disease 2019 (COVID-19) pandemic, New York State ordered the suspension of all elective surgeries to increase intensive care unit (ICU) bed capacity. Yet the potential impact of suspending elective surgery on ICU bed capacity is unclear. METHODS: We retrospectively reviewed 5 years of New York State data on ICU usage. Descriptions of ICU utilization and mechanical ventilation were stratified by admission type (elective surgery, emergent/urgent/trauma surgery, and medical admissions) and by geographic location (New York metropolitan region versus the rest of New York State). Data are presented as absolute numbers and percentages and all adult and pediatric ICU patients were included. RESULTS: Overall, ICU admissions in New York State were seen in 10.1% of all hospitalizations (n = 1,232,986/n = 12,251,617) and remained stable over a 5-year period from 2011 to 2015. Among n = 1,232,986 ICU stays, sources of ICU admission included elective surgery (13.4%, n = 165,365), emergent/urgent admissions/trauma surgery (28.0%, n = 345,094), and medical admissions (58.6%, n = 722,527). Ventilator utilization was seen in 26.3% (n = 323,789/n = 1232,986) of all ICU patients of which 6.4% (n = 20,652), 32.8% (n = 106,186), and 60.8% (n = 196,951) was for patients from elective, emergent, and medical admissions, respectively. New York City holds the majority of ICU bed capacity (70.0%; n = 2496/n = 3566) in New York State. CONCLUSIONS: Patients undergoing elective surgery comprised a small fraction of ICU bed and mechanical ventilation use in New York State. Suspension of elective surgeries in response to the COVID-19 pandemic may thus have a minor impact on ICU capacity when compared to other sources of ICU admission such as emergent/urgent admissions/trauma surgery and medical admissions. More study is needed to better understand how best to maximize ICU capacity for pandemics requiring heavy use of critical care resources.


Assuntos
Agendamento de Consultas , Infecções por Coronavirus/terapia , Cuidados Críticos , Prestação Integrada de Cuidados de Saúde , Procedimentos Cirúrgicos Eletivos , Unidades de Terapia Intensiva/provisão & distribução , Admissão do Paciente , Pneumonia Viral/terapia , Capacidade de Resposta ante Emergências , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/virologia , Bases de Dados Factuais , Necessidades e Demandas de Serviços de Saúde , Humanos , Determinação de Necessidades de Cuidados de Saúde , New York/epidemiologia , Sistemas de Informação em Salas Cirúrgicas , Pandemias , Pneumonia Viral/diagnóstico , Pneumonia Viral/epidemiologia , Pneumonia Viral/virologia , Respiração Artificial , Fatores de Tempo , Ventiladores Mecânicos/provisão & distribução
7.
PLoS One ; 15(10): e0241027, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33085729

RESUMO

As the number of cases of COVID-19 continues to grow, local health services are at risk of being overwhelmed with patients requiring intensive care. We develop and implement an algorithm to provide optimal re-routing strategies to either transfer patients requiring Intensive Care Units (ICU) or ventilators, constrained by feasibility of transfer. We validate our approach with realistic data from the United Kingdom and Spain. In the UK, we consider the National Health Service at the level of trusts and define a 4-regular geometric graph which indicates the four nearest neighbours of any given trust. In Spain we coarse-grain the healthcare system at the level of autonomous communities, and extract similar contact networks. Through random search optimisation we identify the best load sharing strategy, where the cost function to minimise is based on the total number of ICU units above capacity. Our framework is general and flexible allowing for additional criteria, alternative cost functions, and can be extended to other resources beyond ICU units or ventilators. Assuming a uniform ICU demand, we show that it is possible to enable access to ICU for up to 1000 additional cases in the UK in a single step of the algorithm. Under a more realistic and heterogeneous demand, our method is able to balance about 600 beds per step in the Spanish system only using local sharing, and over 1300 using countrywide sharing, potentially saving a large percentage of these lives that would otherwise not have access to ICU.


Assuntos
Betacoronavirus , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/terapia , Recursos em Saúde/provisão & distribução , Modelos Teóricos , Pneumonia Viral/epidemiologia , Pneumonia Viral/terapia , Algoritmos , Infecções por Coronavirus/virologia , Cuidados Críticos , Número de Leitos em Hospital , Humanos , Unidades de Terapia Intensiva/provisão & distribução , Pandemias , Transferência de Pacientes , Pneumonia Viral/virologia , Espanha/epidemiologia , Reino Unido/epidemiologia , Ventiladores Mecânicos/provisão & distribução
8.
Medwave ; 20(9)30-10-2020.
Artigo em Espanhol | LILACS | ID: biblio-1141137

RESUMO

Introducción La pandemia por SARS CoV-2 está presionando fuertemente la capacidad de respuesta de los sistemas de salud en todo el mundo, siendo uno de los aspectos más importantes el aumento masivo de pacientes que requerirán utilizar camas hospitalarias de cuidados intensivos. Objetivo Este estudio propone una metodología para estimar el momento de saturación de las camas de cuidados intensivos hospitalarios (camas críticas) y determinar el número de unidades requeridas para compensar dicha saturación. Método Se analizaron 22 016 pacientes con confirmación diagnóstica para COVID-19 provocada por SARS-CoV-2, entre el 4 de marzo y el 5 de mayo de 2020 a nivel nacional. Sobre la base de información del Ministerio de Salud de Chile y a anuncios ministeriales en medios de prensa, se estimó una disponibilidad total actual de 1900 a 2200 camas críticas totales. Se utilizó la función de Gompertz para estimar el número esperado de pacientes COVID-19 y evaluar su exposición a la oferta disponible de camas de cuidados intensivos en varios escenarios posibles. Para ello se tomó en cuenta la oferta de camas críticas totales, el índice ocupacional promedio, y la demanda de pacientes COVID-19 que requerirán cama de cuidados intensivos. Resultados Considerando diferentes escenarios, entre el 11 y el 27 de mayo podría ser alcanzado el 100% de ocupación de camas críticas totales. Esta condición podría extenderse por unos 48 días dependiendo como se maneje la sobredemanda esperada. Conclusión Se puede establecer una ventana de operaciones relativamente estrecha, de 4 a 8 semanas, para mitigar la inminente saturación de camas críticas hospitalarias, producto de la demanda de pacientes COVID-19.


Introduction SARS CoV-2 pandemic is pressing hard on the responsiveness of health systems worldwide, notably concerning the massive surge in demand for intensive care hospital beds. Aim This study proposes a methodology to estimate the saturation moment of hospital intensive care beds (critical care beds) and determine the number of units required to compensate for this saturation. Methods A total of 22,016 patients with diagnostic confirmation for COVID-19 caused by SARS-CoV-2 were analyzed between March 4 and May 5, 2020, nationwide. Based on information from the Chilean Ministry of Health and ministerial announcements in the media, the overall availability of critical care beds was estimated at 1,900 to 2,000. The Gompertz function was used to estimate the expected number of COVID-19 patients and to assess their exposure to the available supply of intensive care beds in various possible scenarios, taking into account the supply of total critical care beds, the average occupational index, and the demand for COVID-19 patients who would require an intensive care bed. Results A 100% occupancy of critical care beds could be reached between May 11 and May 27. This condition could be extended for around 48 days, depending on how the expected over-demand is managed. Conclusion A simple, easily interpretable, and applicable to all levels (nationwide, regionwide, municipalities, and hospitals) model is offered as a contribution to managing the expected demand for the coming weeks and helping reduce the adverse effects of the COVID-19 pandemic.


Assuntos
Humanos , Pneumonia Viral/epidemiologia , Modelos Estatísticos , Infecções por Coronavirus/epidemiologia , Número de Leitos em Hospital/estatística & dados numéricos , Unidades de Terapia Intensiva/provisão & distribução , Chile/epidemiologia , Pandemias
9.
PLoS One ; 15(9): e0239249, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32960908

RESUMO

Since the end of February 2020 a severe diffusion of COVID-19 has affected Italy and in particular its northern regions, resulting in a high demand of hospitalizations in particular in the intensive care units (ICUs). Hospitals are suffering the high degree of patients to be treated for respiratory diseases and the majority of the health structures, especially in the north of Italy, are or are at risk of saturation. Therefore, the question whether and to what extent the reduction of hospital beds occurred in the past years has biased the management of the emergency has come to the front in the public debate. In our opinion, to start a robust analysis it is necessary to consider the Italian health system capacity prior to the emergency. Therefore, the aim of this study is to analyse the availability of hospital beds across the country as well as to determine their management in terms of complexity and performance of cases treated at regional level. The results of this study underlines that, despite the reduction of beds for the majority of the hospital wards, ICUs availabilities did not change between 2010 and 2017. Moreover, this study confirms that the majority of the Italian regions have a routinely efficient management of their facilities allowing hospitals to treat patients without the risk of having an overabundance of patients and a scarcity of beds. In fact, this analysis shows that, in normal situations, the management of hospital and ICU beds has no critical levels.


Assuntos
Infecções por Coronavirus/terapia , Número de Leitos em Hospital/estatística & dados numéricos , Unidades de Terapia Intensiva/provisão & distribução , Pneumonia Viral/terapia , Betacoronavirus , Infecções por Coronavirus/epidemiologia , Assistência à Saúde/normas , Surtos de Doenças , Número de Leitos em Hospital/normas , Humanos , Unidades de Terapia Intensiva/estatística & dados numéricos , Itália/epidemiologia , Pandemias , Administração dos Cuidados ao Paciente/normas , Pneumonia Viral/epidemiologia
10.
CMAJ ; 192(44): E1347-E1356, 2020 11 02.
Artigo em Inglês | MEDLINE | ID: mdl-32873541

RESUMO

BACKGROUND: To mitigate the effects of coronavirus disease 2019 (COVID-19), jurisdictions worldwide ramped down nonemergent surgeries, creating a global surgical backlog. We sought to estimate the size of the nonemergent surgical backlog during COVID-19 in Ontario, Canada, and the time and resources required to clear the backlog. METHODS: We used 6 Ontario or Canadian population administrative sources to obtain data covering part or all of the period between Jan. 1, 2017, and June 13, 2020, on historical volumes and operating room throughput distributions by surgery type and region, and lengths of stay in ward and intensive care unit (ICU) beds. We used time series forecasting, queuing models and probabilistic sensitivity analysis to estimate the size of the backlog and clearance time for a +10% (+1 day per week at 50% capacity) surge scenario. RESULTS: Between Mar. 15 and June 13, 2020, the estimated backlog in Ontario was 148 364 surgeries (95% prediction interval 124 508-174 589), an average weekly increase of 11 413 surgeries. Estimated backlog clearance time is 84 weeks (95% confidence interval [CI] 46-145), with an estimated weekly throughput of 717 patients (95% CI 326-1367) requiring 719 operating room hours (95% CI 431-1038), 265 ward beds (95% CI 87-678) and 9 ICU beds (95% CI 4-20) per week. INTERPRETATION: The magnitude of the surgical backlog from COVID-19 raises serious implications for the recovery phase in Ontario. Our framework for modelling surgical backlog recovery can be adapted to other jurisdictions, using local data to assist with planning.


Assuntos
Procedimentos Cirúrgicos Cardíacos/estatística & dados numéricos , Infecções por Coronavirus , Neoplasias/cirurgia , Transplante de Órgãos/estatística & dados numéricos , Pandemias , Pneumonia Viral , Procedimentos Cirúrgicos Vasculares/estatística & dados numéricos , Betacoronavirus , Procedimentos Cirúrgicos Eletivos/estatística & dados numéricos , Previsões , Número de Leitos em Hospital/estatística & dados numéricos , Humanos , Unidades de Terapia Intensiva/provisão & distribução , Tempo de Internação/estatística & dados numéricos , Modelos Estatísticos , Ontário , Salas Cirúrgicas/provisão & distribução , Pediatria/estatística & dados numéricos , Fatores de Tempo
11.
Ann Glob Health ; 86(1): 100, 2020 08 13.
Artigo em Inglês | MEDLINE | ID: mdl-32864352

RESUMO

Background: Brazil faces some challenges in the battle against the COVID-19 pandemic, including: the risks for cross-infection (community infection) increase in densely populated areas; low access to health services in areas where the number of beds in intensive care units (ICUs) is scarce and poorly distributed, mainly in states with low population density. Objective: To describe and intercorrelate epidemiology and geographic data from Brazil about the number of intensive care unit (ICU) beds at the onset of COVID-19 pandemic. Methods: The epidemiology and geographic data were correlated with the distribution of ICU beds (public and private health systems) and the number of beneficiaries of private health insurance using Pearson's Correlation Coefficient. The same data were correlated using partial correlation controlled by gross domestic product (GDP) and number of beneficiaries of private health insurance. Findings: Brazil has a large geographical area and diverse demographic and economic aspects. This diversity is also present in the states and the Federal District regarding the number of COVID-19 cases, deaths and case fatality rate. The effective management of severe COVID-19 patients requires ICU services, and the scenario was also dissimilar as for ICU beds and ICU beds/10,000 inhabitants for the public (SUS) and private health systems mainly at the onset of COVID-19 pandemic. The distribution of ICUs was uneven between public and private services, and most patients rely on SUS, which had the lowest number of ICU beds. In only a few states, the number of ICU beds at SUS was above 1 to 3 by 10,000 inhabitants, which is the number recommended by the World Health Organization (WHO). Conclusions: Brazil needed to improve the number of ICU beds units to deal with COVID-19 pandemic, mainly for the SUS showing a late involvement of government and health authorities to deal with the COVID-19 pandemic.


Assuntos
Infecções por Coronavirus , Acesso aos Serviços de Saúde/organização & administração , Unidades de Terapia Intensiva/provisão & distribução , Pandemias , Administração dos Cuidados ao Paciente , Pneumonia Viral , Setor Privado/estatística & dados numéricos , Setor Público/estatística & dados numéricos , Ocupação de Leitos/estatística & dados numéricos , Betacoronavirus , Brasil/epidemiologia , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/terapia , Necessidades e Demandas de Serviços de Saúde , Humanos , Controle de Infecções/organização & administração , Controle de Infecções/normas , Inovação Organizacional , Pandemias/prevenção & controle , Administração dos Cuidados ao Paciente/organização & administração , Administração dos Cuidados ao Paciente/normas , Pneumonia Viral/epidemiologia , Pneumonia Viral/prevenção & controle , Pneumonia Viral/terapia , Índice de Gravidade de Doença
14.
Salvador; s.n; 19 set. 2020. 18 p. ilus, graf, mapas, tab.(Boletim Epidemiológico COVID-19 Bahia, 179).
Monografia em Português | Coleciona SUS, CONASS, SES-BA | ID: biblio-1121677

RESUMO

Panorama da pandemia COVID-19 no Estado da Bahia, em 19 de setembro de 2020. Descreve de forma detalhada a situação da pandemia no Estado, contempla informações relacionadas ao registro de casos notificados da COVID-19, taxa de crescimento, distribuição de casos confirmados nos Núcleos Regionais Saúde, casos confirmados segundo raça/cor, ocupação de leitos de UTI, perfil dos casos de Síndrome Multissistêmica Pediátrica, número de curados, número de óbitos


Assuntos
Humanos , Masculino , Feminino , Pneumonia Viral/epidemiologia , Infecções por Coronavirus/mortalidade , Infecções por Coronavirus/epidemiologia , Monitoramento Epidemiológico , Betacoronavirus , Unidades de Terapia Intensiva/provisão & distribução , Ocupação de Leitos , Pandemias/prevenção & controle
15.
Eur J Epidemiol ; 35(8): 733-742, 2020 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-32780189

RESUMO

Forecasting models have been influential in shaping decision-making in the COVID-19 pandemic. However, there is concern that their predictions may have been misleading. Here, we dissect the predictions made by four models for the daily COVID-19 death counts between March 25 and June 5 in New York state, as well as the predictions of ICU bed utilisation made by the influential IHME model. We evaluated the accuracy of the point estimates and the accuracy of the uncertainty estimates of the model predictions. First, we compared the "ground truth" data sources on daily deaths against which these models were trained. Three different data sources were used by these models, and these had substantial differences in recorded daily death counts. Two additional data sources that we examined also provided different death counts per day. For accuracy of prediction, all models fared very poorly. Only 10.2% of the predictions fell within 10% of their training ground truth, irrespective of distance into the future. For accurate assessment of uncertainty, only one model matched relatively well the nominal 95% coverage, but that model did not start predictions until April 16, thus had no impact on early, major decisions. For ICU bed utilisation, the IHME model was highly inaccurate; the point estimates only started to match ground truth after the pandemic wave had started to wane. We conclude that trustworthy models require trustworthy input data to be trained upon. Moreover, models need to be subjected to prespecified real time performance tests, before their results are provided to policy makers and public health officials.


Assuntos
Infecções por Coronavirus/mortalidade , Previsões/métodos , Unidades de Terapia Intensiva/estatística & dados numéricos , Pandemias/prevenção & controle , Pneumonia Viral/mortalidade , Ocupação de Leitos , Betacoronavirus , Humanos , Unidades de Terapia Intensiva/provisão & distribução , Modelos Estatísticos , Mortalidade/tendências , New York/epidemiologia , Saúde Pública
16.
Oncology (Williston Park) ; 34(8): 317-319, 2020 08 12.
Artigo em Inglês | MEDLINE | ID: mdl-32785928

RESUMO

A 78-year-old man had a medical history of hypertension, atrial fibrillation, chronic kidney disease, and metastatic castration-resistant prostate cancer (CRPC). He had progressed to first-line therapy for CRPC with abiraterone plus androgen-deprivation therapy (ADT) and as second-line therapy he was being treated with docetaxel, with biochemical progression in his last prostate specific antigen measurement. He was admitted to the hospital on April 2020, in the middle of the coronavirus disease 2019 (COVID-19) pandemic, because of painful bone lesions and deterioration of renal function.


Assuntos
Anticoagulantes/uso terapêutico , Neoplasias Ósseas/tratamento farmacológico , Infecções por Coronavirus/terapia , Cuidados Paliativos , Pneumonia Viral/terapia , Neoplasias de Próstata Resistentes à Castração/tratamento farmacológico , Insuficiência Respiratória/terapia , Idoso , Antagonistas de Androgênios/uso terapêutico , Androstenos/uso terapêutico , Antineoplásicos/uso terapêutico , Betacoronavirus , Conservadores da Densidade Óssea/uso terapêutico , Neoplasias Ósseas/complicações , Neoplasias Ósseas/secundário , Dor do Câncer/complicações , Dor do Câncer/terapia , Infecções por Coronavirus/complicações , Progressão da Doença , Docetaxel/uso terapêutico , Combinação de Medicamentos , Definição da Elegibilidade , Heparina de Baixo Peso Molecular/uso terapêutico , Humanos , Unidades de Terapia Intensiva/provisão & distribução , Lopinavir/uso terapêutico , Masculino , Oxigenoterapia , Pandemias , Pneumonia Viral/complicações , Neoplasias de Próstata Resistentes à Castração/complicações , Neoplasias de Próstata Resistentes à Castração/patologia , Insuficiência Renal , Insuficiência Respiratória/etiologia , Reação em Cadeia da Polimerase Via Transcriptase Reversa , Ritonavir/uso terapêutico , Índice de Gravidade de Doença , Ácido Zoledrônico/uso terapêutico
17.
Am J Kidney Dis ; 76(5): 696-709.e1, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-32730812

RESUMO

RATIONALE & OBJECTIVE: During the coronavirus disease 2019 (COVID-19) pandemic, New York encountered shortages in continuous kidney replacement therapy (CKRT) capacity for critically ill patients with acute kidney injury stage 3 requiring dialysis. To inform planning for current and future crises, we estimated CKRT demand and capacity during the initial wave of the US COVID-19 pandemic. STUDY DESIGN: We developed mathematical models to project nationwide and statewide CKRT demand and capacity. Data sources included the Institute for Health Metrics and Evaluation model, the Harvard Global Health Institute model, and published literature. SETTING & POPULATION: US patients hospitalized during the initial wave of the COVID-19 pandemic (February 6, 2020, to August 4, 2020). INTERVENTION: CKRT. OUTCOMES: CKRT demand and capacity at peak resource use; number of states projected to encounter CKRT shortages. MODEL, PERSPECTIVE, & TIMEFRAME: Health sector perspective with a 6-month time horizon. RESULTS: Under base-case model assumptions, there was a nationwide CKRT capacity of 7,032 machines, an estimated shortage of 1,088 (95% uncertainty interval, 910-1,568) machines, and shortages in 6 states at peak resource use. In sensitivity analyses, varying assumptions around: (1) the number of pre-COVID-19 surplus CKRT machines available and (2) the incidence of acute kidney injury stage 3 requiring dialysis requiring CKRT among hospitalized patients with COVID-19 resulted in projected shortages in 3 to 8 states (933-1,282 machines) and 4 to 8 states (945-1,723 machines), respectively. In the best- and worst-case scenarios, there were shortages in 3 and 26 states (614 and 4,540 machines). LIMITATIONS: Parameter estimates are influenced by assumptions made in the absence of published data for CKRT capacity and by the Institute for Health Metrics and Evaluation model's limitations. CONCLUSIONS: Several US states are projected to encounter CKRT shortages during the COVID-19 pandemic. These findings, although based on limited data for CKRT demand and capacity, suggest there being value during health care crises such as the COVID-19 pandemic in establishing an inpatient kidney replacement therapy national registry and maintaining a national stockpile of CKRT equipment.


Assuntos
Lesão Renal Aguda , Defesa Civil , Terapia de Substituição Renal Contínua/métodos , Infecções por Coronavirus , Estado Terminal , Necessidades e Demandas de Serviços de Saúde/organização & administração , Unidades de Terapia Intensiva/provisão & distribução , Pandemias , Pneumonia Viral , Estoque Estratégico/métodos , Lesão Renal Aguda/etiologia , Lesão Renal Aguda/terapia , Betacoronavirus , Defesa Civil/métodos , Defesa Civil/organização & administração , Infecções por Coronavirus/complicações , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/terapia , Estado Terminal/epidemiologia , Estado Terminal/terapia , Humanos , Modelos Teóricos , Pneumonia Viral/complicações , Pneumonia Viral/epidemiologia , Pneumonia Viral/terapia , Utilização de Procedimentos e Técnicas/estatística & dados numéricos , Medição de Risco/métodos , Estados Unidos/epidemiologia
18.
PLoS One ; 15(7): e0236308, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32687538

RESUMO

INTRODUCTION: The COVID-19 pandemic will test the capacity of health systems worldwide and especially so in low- and middle-income countries. The objective of this study was to assess the surge capacity of the Kenyan of the Kenyan health system in terms of general hospital and ICU beds in the face of the COVID-19 pandemic. METHODS: We assumed that 2% of the Kenyan population get symptomatic infection by SARS-Cov-2 based on modelled estimates for Kenya and determined the health system surge capacity for COVID-19 under three transmission curve scenarios, 6, 12, and 18 months. We estimated four measures of hospital surge capacity namely: 1) hospital bed surge capacity 2) ICU bed surge capacity 3) Hospital bed tipping point, and 5) ICU bed tipping point. We computed this nationally and for all the 47 county governments. RESULTS: The capacity of Kenyan hospitals to absorb increases in caseload due to COVID-19 is constrained by the availability of oxygen, with only 58% of hospital beds in hospitals with oxygen supply. There is substantial variation in hospital bed surge capacity across counties. For example, under the 6 months transmission scenario, the percentage of available general hospital beds that would be taken up by COVID-19 cases varied from 12% Tharaka Nithi county, to 145% in Trans Nzoia county. Kenya faces substantial gaps in ICU beds and ventilator capacity. Only 22 out of the 47 counties have at least 1 ICU unit. Kenya will need an additional 1,511 ICU beds and 1,609 ventilators (6 months transmission curve) to 374 ICU beds and 472 ventilators (18 months transmission curve) to absorb caseloads due to COVID-19. CONCLUSION: Significant gaps exist in Kenya's capacity for hospitals to accommodate a potential surge in caseload due to COVID-19. Alongside efforts to slow and supress the transmission of the infection, the Kenyan government will need to implement adaptive measures and additional investments to expand the hospital surge capacity for COVID-19. Additional investments will however need to be strategically prioritized to focus on strengthening essential services first, such as oxygen availability before higher cost investments such as ICU beds and ventilators.


Assuntos
Infecções por Coronavirus/epidemiologia , Número de Leitos em Hospital , Pneumonia Viral/epidemiologia , Capacidade de Resposta ante Emergências , Ventiladores Mecânicos/provisão & distribução , Betacoronavirus , Humanos , Unidades de Terapia Intensiva/provisão & distribução , Quênia/epidemiologia , Pandemias
19.
J Biol Dyn ; 14(1): 621-632, 2020 12.
Artigo em Inglês | MEDLINE | ID: mdl-32715932

RESUMO

We model the extent to which age-targeted protective sequestration can be used to reduce ICU admissions caused by novel coronavirus COVID-19. Using demographic data from New Zealand, we demonstrate that lowering the age threshold to 50 years of age reduces ICU admissions drastically and show that for sufficiently strict isolation protocols, sequestering one-third of the countries population for a total of 8 months is sufficient to avoid overwhelming ICU capacity throughout the entire course of the epidemic. Similar results are expected to hold for other countries, though some minor adaption will be required based on local age demographics and hospital facilities.


Assuntos
Betacoronavirus , Infecções por Coronavirus/epidemiologia , Modelos Biológicos , Pandemias , Pneumonia Viral/epidemiologia , Quarentena/métodos , Adolescente , Adulto , Fatores Etários , Idoso , Idoso de 80 Anos ou mais , Criança , Pré-Escolar , Controle de Doenças Transmissíveis/métodos , Simulação por Computador , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/transmissão , Cuidados Críticos , Feminino , Hospitalização , Humanos , Lactente , Recém-Nascido , Unidades de Terapia Intensiva/provisão & distribução , Masculino , Pessoa de Meia-Idade , Nova Zelândia/epidemiologia , Pandemias/prevenção & controle , Isolamento de Pacientes/métodos , Pneumonia Viral/prevenção & controle , Pneumonia Viral/transmissão , Fatores de Risco , Adulto Jovem
20.
Infect Dis Health ; 25(4): 227-232, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-32631682

RESUMO

BACKGROUND: Low-resource countries with fragile healthcare systems lack trained healthcare professionals and specialized resources for COVID-19 patient hospitalization, including mechanical ventilators. Additional socio-economic complications such as civil war and financial crisis in Libya and other low-resource countries further complicate healthcare delivery. METHODS: A cross-sectional survey evaluating hospital and intensive care unit's capacity and readiness was performed from 16 leading Libyan hospitals in March 2020. In addition, a survey was conducted among 400 doctors who worked in these hospitals to evaluate the status of personal protective equipment. RESULTS: Out of 16 hospitals, the highest hospital capacity was 1000 in-patient beds, while the lowest was 25 beds with a median of 200 (IQR 52-417, range 25-1000) hospital beds. However, a median of only eight (IQR 6-14, range 3-37) available functioning ICU beds were reported in these hospitals. Only 9 (IQR 4.5-14, range 2-20) mechanical ventilators were reported and none of the hospitals had a reverse transcription-polymerase chain reaction machine for COVID-19 testing. Moreover, they relied on one of two central laboratories located in major cities. Our PPE survey revealed that 56.7% hospitals lacked PPE and 53% of healthcare workers reported that they did not receive proper PPE training. In addition, 70% reported that they were buying the PPE themselves as hospitals did not provide them. CONCLUSION: This study provides an alarming overview of the unpreparedness of Libyan hospitals for detecting and treating patients with COVID-19 and limiting the spread of the pandemic.


Assuntos
Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/terapia , Recursos em Saúde/provisão & distribução , Unidades de Terapia Intensiva/provisão & distribução , Pneumonia Viral/diagnóstico , Pneumonia Viral/terapia , Betacoronavirus/isolamento & purificação , Técnicas de Laboratório Clínico/estatística & dados numéricos , Infecções por Coronavirus/epidemiologia , Estudos Transversais , Assistência à Saúde/estatística & dados numéricos , Instalações de Saúde/estatística & dados numéricos , Instalações de Saúde/provisão & distribução , Pessoal de Saúde/estatística & dados numéricos , Hospitais/estatística & dados numéricos , Hospitais/provisão & distribução , Humanos , Unidades de Terapia Intensiva/estatística & dados numéricos , Líbia/epidemiologia , Pandemias , Equipamento de Proteção Individual/estatística & dados numéricos , Equipamento de Proteção Individual/provisão & distribução , Pneumonia Viral/epidemiologia , Inquéritos e Questionários , Ventiladores Mecânicos/provisão & distribução , Organização Mundial da Saúde
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